In this paper, similar to [1]}, we first place PMUs optimally to obtain a fully-observed node feature matrix; however, we do not create a separate
factor-graph (the reason is given in Section ).
Then, we build a GNN in which the physical connections of the power system are embedded using multiple convolution layers and important node features are extracted via an attention layer, to perform high-speed time-synchronized SE.
Finally, we demonstrate the robustness of the proposed GNN-based state estimator compared with the state-of-the-art for high-speed SE, namely, model-based LSE and regular DNN-based SE, in terms of handling non-Gaussian measurement noise and topology changes, respectively.
